Model-Dependent Behavioral Diversity

• Model-dependent behavioral diversity is the variability in agent behaviors emerging from specific model structures, algorithmic rules, and parameter settings.
• This concept quantifies diversity through hierarchical teaching, parameter tuning, and measurable metrics applied in cultural evolution, robotics, and software testing.
• Structured models induce controlled synthesis of complex behaviors by constraining learning mechanisms and ensuring robust emergent patterns across systems.

Model-dependent behavioral diversity refers to the diversity in observed behaviors of agents or populations that emerges as a function of the specific structural, algorithmic, or parameter choices within a generative or learning model. The term captures the fact that diversity is not merely a product of agent heterogeneity or random variation but is fundamentally shaped—constrained or enabled—by the representational assumptions, dependencies, and mechanisms built into the underlying model. This concept is central in fields ranging from cultural evolution and multi-agent systems to testing, planning, robotics, and cognition: in each case, the spectrum and structure of behavioral diversity observed is tightly linked to the rules and architecture of the model generating it.

1. Foundations in Transmission and Learning Models

The emergence of model-dependent behavioral diversity is exemplified by extensions to classic cultural transmission models. In the “semantic Axelrod model,” cultural traits are no longer unstructured but are embedded in hierarchical trait trees with explicit prerequisite relationships. Learning is constrained such that individuals can only acquire a trait if all parent (prerequisite) traits are present; if not, a structured teaching mechanism may impart the missing prerequisites with probability 𝒫(l). The behavioral diversity that arises—depth and richness of cultural repertoires, the differentiation among cultural clusters—is tightly controlled by model parameters such as the structure of trait graphs and the teaching probability. Simulations demonstrate that richer, deeper cultural configurations and greater population-wide diversity emerge primarily when these parameters favor high-fidelity, prerequisite-sensitive teaching, rather than unstructured imitation alone (Madsen et al., 2014).

2. Mechanisms and Metrics for Quantifying Behavioral Diversity

Behavioral diversity is rigorously quantified in a model-dependent manner by metrics that directly reflect the system’s generative structure. In robotics and multi-agent systems, frameworks utilize behavior assemblages built from finite state machines and combinations of motor schemas (potential fields encompassing both attraction and repulsion dynamics), permitting even homogeneous hardware to manifest heterogeneous behavior by software design. Here, the diversity of a team is not a given but is induced and measured by variation in behavior sequences, state transition rules, or weighting of motor schemas (Dragoicea, 2015). In evolutionary game theory, multi-choice strategy sets (memory-1 behaviors, as in iterated games) result in rugged fitness landscapes with numerous, model-constrained robust equilibria: the breadth, stability, and nature of diversity is thus a function of available choice structure and evolutionary updating rules (Stewart et al., 2016). In software testing, behavioral diversity is empirically measured by running tests against systematically mutated program versions and recording distinctiveness in pass/fail patterns, with quantitative distances such as accuracy or Matthews correlation coefficient directly capturing behavioral differentiation induced by test suite and mutation model (Neto et al., 2020).

3. Implications of Structured Models for Diversity Dynamics

Model-dependent behavioral diversity arises from explicit model features such as prerequisite graphs, hierarchical learning rules, parameterized teaching fidelity, or specific reward and exploration structures. For example, in the semantic Axelrod model, prerequisite structures induce path dependency: an individual’s early learning history strongly constrains future behavioral repertoires, leading to less symmetric, more specialized cultural configurations as teaching fidelity increases. Similarly, in working with adaptive agent-based networks, diversity in parameters like cultural tolerance, state change rate, or edge weight update functions can lead to macro-level social states where high cultural diversity coexists with strong structural connectivity—an outcome only possible in models accommodating behavioral heterogeneity in these parameters (Sayama et al., 2019). These sensitivities to model specification underscore that “diversity” is not merely the result of stochasticity or external variation, but an emergent property of specific generative and interactional rules.

4. Consequences and Observed Patterns in Realistic Systems

Model-dependent diversity has far-reaching real-world implications. In cultural evolution, shifts in archaeological tool kit complexity and differentiation are traced to increases in teaching-structured transmission modes, per the implications of trait tree models (Madsen et al., 2014). In multi-robot systems, task effectiveness is enhanced by explicitly engineered behavioral diversity through modular FSMs and motor schema design (Dragoicea, 2015). Social network evolution models demonstrate that only certain distributions of behavioral traits (notably, broad distributions of cultural tolerance) maintain cultural diversity while ensuring network connectivity (Sayama et al., 2019). In testing or fuzzing domains, systems like BeDivFuzz show that only by separating structure-affecting and structure-preserving variations within behavioral models does one achieve both coverage and evenness, leading to a more robust behavioral assessment (Nguyen et al., 2022).

5. Path-Dependency, Role Formation, and Algorithmic Sensitivity

A defining haLLMark of model-dependent behavioral diversity is its sensitivity to historical contingency and interactional orderings encoded in the model. The sequence of acquired traits or behaviors—dictated by hierarchical dependencies—can limit or expand the richness of repertoires, resulting in differentiated “cultural” or “behavioral” roles that are robust only when certain model parameters are tuned. Minor increases in structured teaching probability, network interaction models, or individual innovation rates can transform a system from homogeneous (monoculture, uniform test outcomes, or convergent robotic strategies) to polycultural or polymorphic, with higher-order differentiation observable at the population level. Algebraic measures (such as automorphism group size in trait trees) and rigorous metrics (e.g., mean radius, Hill numbers, Wasserstein distances) formalize such effects, linking diversity directly to model specification.

6. Broader Theoretical and Practical Significance

The paper of model-dependent behavioral diversity traverses cognitive science, artificial life, collective robotics, software engineering, and planning. It underscores the necessity of designing models, algorithms, and evaluation metrics that do not merely admit diversity but shape, constrain, and explain its emergence. In all domains, the granularity and nature of diversity observed are not system-invariant: they arise from the interplay of hierarchical structuring, model parameterization, and explicit representation of dependencies and constraints. Consequently, progress in producing, maintaining, or even measuring diversity—whether for robustness, resilience, or innovation—can only proceed with attention to the underlying generative model’s structure and dynamics.

7. Conclusion

Model-dependent behavioral diversity conceptualizes diversity as an emergent, parameterizable property of specific model architectures—transmission rules, learning mechanisms, network structures, and interaction protocols. Its properties and magnitude are fundamentally tied to the details of information representation, the order and probability of social learning, and rules for prerequisite satisfaction and innovation. Through mathematical formalization (such as Jaccard overlap indices, trait automorphism groups, regression models quantifying attribute variability, and diversity-focused planning algorithms), model-dependent diversity enables both explanation and controlled synthesis of complex, differentiated behaviors across cultural, biological, and artificial systems.